Hands-on - Visualizations with R

Borrelsessie IM/ICT

From R. A. Fisher, 1938: To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of

Some inspiring examples

A recreation of the Gapminder Bubble Graph, with facets per continent

source(here::here("code", "08_gganimate_demo.R"))
gapminder_animation

Resources & references

https://simplystatistics.org/2018/01/22/the-dslabs-package-provides-datasets-for-teaching-data-science/

https://github.com/rbind/simplystats

link to free online book “r4ds”: “R for Data Science”, Hadley Wickham & Garret Grolemund

Gapminder Bubble Chart

https://www.gapminder.org/tools/#$state$time$value=2018;;&chart-type=bubbles

The ggplot2 packages

I find the the ggplot2 package for R the best plotting system for R. It’s syntax is an implementation of the ‘grammar of graphics’ and all plots in this demo are created by using ggplot2.

To cite ggplot2 in your work run citation("ggplot2") in R to get the reference details.

The 3rd edition of the free online book resource for ggplot2: “ggplot2 Elegant Graphics for Data Analysis”

Citations

citation(package = "ggplot2")
citation(package = "tidyverse")
citation(package = "dslabs")

Installing data package

install.packages("dslabs")

Loading the data package {dslabs} and other packages used

library(tidyverse)
library(dslabs)
library(lubridate)

Loading a specific dataset by name

data(package = "dslabs")
data("us_contagious_diseases", package = "dslabs")

unique(us_contagious_diseases$disease)

## [1] Hepatitis A Measles     Mumps       Pertussis   Polio       Rubella    
## [7] Smallpox   
## Levels: Hepatitis A Measles Mumps Pertussis Polio Rubella Smallpox

## ?gapminder for more info on the variables in the dataset

Measles example

Every data cycle starts with a question / problem:

• Problem
• Plan
• Data
• Analysis
• Conclusion

 DiagrammeR::grViz("
digraph rmarkdown {
  Problem -> Plan
  Plan -> Data
  Data -> Analysis
  Analysis -> Conclusion 
  Conclusion -> Problem
}
")

Was the introduction of the vaccination against the measles effective?

library(dslabs)
data(us_contagious_diseases)
the_disease <- "Measles"
dat <- us_contagious_diseases %>%
  dplyr::filter(!state%in%c("Hawaii","Alaska") &  
      disease == the_disease) %>%
  mutate(rate = count / population * 10000 * 52 / weeks_reporting) 

jet.colors <- colorRampPalette(c("#F0FFFF", "cyan", "#007FFF", "yellow", "#FFBF00", "orange", "red", "#7F0000"), bias = 2.25)

dat %>% mutate(state = reorder(state, desc(state))) %>%
  ggplot(aes(year, state, fill = rate)) +
  geom_tile(color = "white", size = 0.35) +
  scale_x_continuous(expand = c(0,0)) +
  scale_fill_gradientn(colors = jet.colors(16), na.value = 'white') +
  geom_vline(xintercept = 1963, col = "black") +
  theme_minimal() + 
  theme(panel.grid = element_blank()) +
  coord_cartesian(clip = 'off') +
  ggtitle(the_disease) +
  ylab("") +
  xlab("") +  
  theme(legend.position = "bottom", text = element_text(size = 8)) + 
  annotate(geom = "text", x = 1963, y = 50.5, label = "Vaccine introduced", size = 3, hjust = 0)

Build upon existing code

Try changing the line in the code chunk above from

the_disease <- "Measles" (Mazelen in Dutch) to

the_disease <- "Smallpox" (Pokken in Dutch) and rerun the code.

Can you find out which part of the code above plots the vertical line and prints the words “Vaccine introduced”. Try to adapt the figure for the Smallpox data: The introduction of the smallpox vaccine was already as early as in 1940. TIP You will need to change the year 1963 twice to 1940 in oder for the new figure to be correct for the Smallpox subset of the data. Try adapting the code and rerun the code chunk.

Question: When did vaccination against smallpox stop? Try googling for the answer!

Exploring the Gapminder dataset in R

The gapminder dataset contains a number of measurements on health and income outcomes for 184 countries from 1960 to 2016. It also includes two character vectors, OECD and OPEC, with the names of OECD and OPEC countries from 2016.

Inspecting the dataset with R

gapminder <- gapminder %>% as_tibble()
gapminder %>% head(2)

## # A tibble: 2 x 9
##   country  year infant_mortality life_expectancy fertility population
##   <fct>   <int>            <dbl>           <dbl>     <dbl>      <dbl>
## 1 Albania  1960             115.            62.9      6.19    1636054
## 2 Algeria  1960             148.            47.5      7.65   11124892
## # ... with 3 more variables: gdp <dbl>, continent <fct>, region <fct>

names(gapminder)

## [1] "country"          "year"             "infant_mortality"
## [4] "life_expectancy"  "fertility"        "population"      
## [7] "gdp"              "continent"        "region"

A very simple example to start with

gapminder %>% 
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point()

Fixing overplotting

We call this ‘overplotting’.

This can be fixed in several ways:

• Reducing the transparency of data points
  
• Mapping colour to a variable (continuous or categorical)
• Reduce the data in the plot
• Mapping a shape to a variable
• Add noise ("jitter") to points
• Facetting - create panels for ‘categorical’ or so-called ‘factor’ variables in R
• Summarize the data
• Displaying a model / relationship that represents the data (and not sho the actual data itself)
• Or any combination of the above strategies

Basically you map an aesthetic (aes()) or a characteristic to a variable

Let’s go over these overplotting methods one by one

1. Overplotting: Reducing transparency (alpha) of points or lines in the data

gapminder %>% 
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(alpha = 0.1)

2. Mapping colour to a variable (continuous or categorical)

gapminder %>% 
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(aes(colour = continent))

or combined with alpha

gapminder %>% 
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(aes(colour = continent), alpha = 0.1) +
  guides(colour = guide_legend(override.aes = list(alpha = 1)))

Do it yourself:

• Try adjusting some of the arguments in the previous ggplot2 call. For example, adjust the alpha = ... or change the variable in x = ..., y = ... or colour = ...
• names(gapminder) gives you the variable names that you can change
• Show and discuss the resulting plot with your neighbour
• What do you think this part does:

guides(colour = guide_legend(override.aes = list(alpha = 1)))

• Try to find out by disabling with #

3. Reduce the data in the plot

reduce_data_plot <- gapminder %>% 
  filter(continent == "Africa" | continent == "Europe") %>%
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(aes(colour = continent), alpha = 0.2) +
  ## override the alpha setting for the points in the legend:
  guides(colour = guide_legend(override.aes = list(alpha = 1))) 

reduce_data_plot

Discuss with you neighbour:

• What does the the aes() part of the geom_point() do?
• Compare the code below with the code above, can you spot the difference, what is the advantage of the code below?

reduce_data_plot <- gapminder %>% 
  filter(continent == "Africa" | continent == "Europe") %>%
  ggplot(aes(x = fertility,
             y = life_expectancy, colour = continent)) +
  geom_point(alpha = 0.2) +
  ## override the alpha setting for the points in the legend:
  guides(colour = guide_legend(override.aes = list(alpha = 1))) 

4. Mapping a shape to a variable

## or e.g. show only two years and map a shape to continent
shape_plot <- gapminder %>% 
  dplyr::filter(continent == "Africa" | continent == "Europe",
         year == "1960" | year == "2010") %>%
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(aes(colour = as_factor(as.character(year)), 
                 shape = continent), 
             alpha = 0.7)

shape_plot

Do it youself

• Try removing the as_factor(as.character(year)) call and replace this by only year above and rerun the plot, what happened?

5. Facetting

Create panels for ‘categorical’ or so-called ‘factor’ variables in R

facets_plot <- gapminder %>% 
  dplyr::filter(continent == "Africa" | continent == "Europe",
         year == "1960" | year == "2010") %>%
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(aes(colour = continent), alpha = 0.5) +
  facet_wrap(~ year)

facets_plot

6. Summarize the data

library(ggrepel)

years <- c("1960", "1970", "1980", "1990", "2000", "2010")

summarize_plot <- gapminder %>% 
  dplyr::filter(year %in% years) %>%
  group_by(continent, year) %>%
  summarise(mean_life_expectancy = mean(life_expectancy),
            mean_fertility = mean(fertility)) %>%
  ggplot(aes(x = mean_fertility,
             y = mean_life_expectancy)) +
  geom_point(aes(colour = continent), alpha = 0.7) 

summarize_plot

Adding labels to points with {ggrepel}

library(ggrepel)

years <- c("1960", "1970", "1980", "1990", "2000", "2010")

labels_plot <- gapminder %>% 
  dplyr::filter(year %in% years) %>%
  group_by(continent, year) %>%
  summarise(mean_life_expectancy = mean(life_expectancy),
            mean_fertility = mean(fertility)) %>%
  ggplot(aes(x = mean_fertility,
             y = mean_life_expectancy)) +
  geom_point(aes(colour = continent), alpha = 0.7) +
  geom_label_repel(aes(label=year), size = 2.5, box.padding = .5)
  

labels_plot

7. Displaying a model / relationship that represents the data (and not show the actual data itself)

## Model
lm <- gapminder %>% lm(formula = life_expectancy ~ fertility)

correlation <- cor.test(x = gapminder$fertility, y = gapminder$life_expectancy, method = "pearson")

# save predictions of the model in the new data frame 
# together with variable you want to plot against
predicted_df <- data.frame(gapminder_pred = predict(lm, gapminder), 
                           fertility = gapminder$fertility)

Add model to plot

model_plot <- gapminder %>% 
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
#  geom_point(alpha = 0.03) +
  geom_line(data = predicted_df, aes(x = fertility, 
                                     y = gapminder_pred),
            colour = "darkred", size = 1)
model_plot

Plotting statistics to the graph with the {ggpubr} package

Using a smoother geom_smooth to display potential relationships

gapminder %>% 
  ggplot(aes(x = fertility,
             y = life_expectancy)) +
  geom_point(alpha = 0.02) +
  geom_smooth(method = "lm") +
  stat_cor(method = "pearson", label.x = 2, label.y = 30) +
  theme_bw()

Recap Discuss with your neighbour

Which tricks can we use to reduce the dimensionality of the plotted data (prevent overpltting)?

Try listing at least 6 methods:

Relation between gdp, Gross Domestic Product and infant_mortality rate.

https://en.wikipedia.org/wiki/Gross_domestic_product Wikipedia: Gross Domestic Product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, often annually or quarterly. Nominal GDP estimates are commonly used to determine the economic performance of a whole country or region, and to make international comparisons.

gdp_infant_plot <- gapminder %>%
  dplyr::filter(continent == "Europe" | continent == "Africa") %>%
  ggplot(aes(x = gdp, 
             y = infant_mortality)) +
  geom_point() 
gdp_infant_plot

Adding a bit of colour

The figure above does not provide any clue on a possible difference between Europe and Africa, nor does it convey any information on trends over time.

colour_to_continent <- gapminder %>%
  dplyr::filter(continent == "Europe" | continent == "Africa") %>%
  ggplot(aes(x = gdp, 
             y = infant_mortality)) +
  geom_point(aes(colour = continent))

colour_to_continent

Adding facets

Let’s investigate whether things have improved over time. We compare 1960 to 2010 by using a panel of two figures. Adding simply facet_wrap( ~ facetting_variable) will do the trick.

Discuss with your neighbour:

Without looking ahead try to contruct a plot that conveys information on the gdp per continent, over time. Try to recycle some of the examples above.

facets_gdp_infant <- gapminder %>%
  dplyr::filter(continent == "Europe" | continent == "Africa",
                year == "1960" | year == "2010") %>%
  ggplot(aes(x = gdp, 
             y = infant_mortality)) +
  geom_point(aes(colour = continent)) +
  facet_wrap(~ year) +
  theme(axis.text.x = element_text(angle = -90, hjust = 1))
  

facets_gdp_infant

Mapping to continuous variables

So far we have been mapping colours and shapes to categorical variables. You can also map to continuous variables though.

continuous <- gapminder %>%
  dplyr::filter(country == "Netherlands" | 
                country == "China" |
                country == "India") %>%
  dplyr::filter(year %in% years) %>%
  ggplot(aes(x = year,
         y = life_expectancy)) +
  geom_point(aes(size = population, colour = country)) +
  guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  geom_line(aes(group = country)) +
  theme_bw()

continuous

Discuss with your neighbour

Try plotting the infant_mortality against the filtered years for the same countries as the code above (Netherlands, India, China), recycling some of the code above. Discuss the resulting graph in the light of the life_expectancy graph, what do you think about the the developments in China?

Want to know more? see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331212/ Babxiarz, 2016

Discuss with your neighbour

Analyze the following code chunk: try running line by line to see what happens:

• How many observations are we plotting here?
• How many variables are we plotting?
• Try adding or removing variables to the group_by() statement, what happens if you do?

Summarize per continent and sum population

population_plot <- gapminder %>% 
  dplyr::group_by(continent, year) %>%
  dplyr::filter(year %in% years) %>%
  summarise(sum_population = sum(population)) %>% 
  ggplot(aes(x = year, 
             y = sum_population)) +
    geom_point(aes(colour = continent)) +
    geom_line(aes(group = continent,
                  colour = continent))
population_plot

Ranking data

ranking_plot <- gapminder %>%
  dplyr::filter(continent == "Europe",
                year == 2010) %>%
  ggplot(aes(x = reorder(as_factor(country), population),
             y = log10(population))) +
  geom_point() +
  ylab("log10(Population)") +
  xlab("Country") + 
  coord_flip() +
  geom_point(data = filter(gapminder %>%
  dplyr::filter(continent == "Europe",
                year == 2010), population >= 1e7), colour = "red")

ranking_plot

Let’s look at a time series

We filter for “Americas” and “Oceania” and look at life_expectancy over the years.

## without summarizing for countries
gapminder$continent %>% as_factor() %>% levels()

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

gapminder %>% 
  dplyr::filter(continent == "Americas" | continent == "Oceania") %>%
  ggplot(aes(x = year,
             y = life_expectancy)) +
  geom_line(aes(group = continent,
                colour = continent))

Obviously something went wrong here. Please, discuss with your neighbour what you think happened or needs to be done to fix this (without looking ahead ;-) )

Grouping

We can see what happened if we plot individual datapoints

gapminder %>% 
  dplyr::filter(continent == "Americas" | continent == "Oceania") %>%
  ggplot(aes(x = year,
             y = life_expectancy)) +
  geom_point(aes(colour = country)) +
  theme(legend.position="none") +
  facet_wrap( ~ continent) +
  theme(legend.position="none") 

Summarizing time series data

gapminder$continent %>% as_factor() %>% levels()

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

gapminder %>% 
  dplyr::filter(continent == "Americas" | continent == "Oceania") %>%
  group_by(continent, year) %>%
  summarise(mean_life_expectancy = mean(life_expectancy)) %>%
  ggplot(aes(x = year,
             y = mean_life_expectancy)) +
  geom_line(aes(group = continent,
                colour = continent)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Statistical proof?

df <- gapminder %>% 
  dplyr::filter(continent == "Americas" | continent == "Oceania") %>%
  group_by(continent, year)

model <- aov(data = df, life_expectancy ~ continent * year)
anova(model)

## Analysis of Variance Table
## 
## Response: life_expectancy
##                  Df Sum Sq Mean Sq  F value Pr(>F)    
## continent         1   8982    8982  269.104 <2e-16 ***
## year              1  58606   58606 1755.931 <2e-16 ***
## continent:year    1      9       9    0.278 0.5981    
## Residuals      2732  91183      33                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Some remarks on the above Two-way ANOVA:

• Repeated measures / multilevel models might be more appropriate here (paired / nested designs)
• We did not perform any check on assumptions
• We performed our analysis on only part of the data

One more option: categorical values and “jitter”

Sometimes you have overlapping plots and adding transparency with alpha() or mapping colour to underlying categorical values is not working because there are simple to many points overlapping

Let’s look at an example

gapminder %>% 
  dplyr::filter(continent == "Americas" |
                continent == "Africa") %>%
  group_by(continent) %>%
  dplyr::filter(year %in% years) %>%
  ggplot(aes(x = year,
             y = infant_mortality)) +
  geom_point(aes(colour = country)) +
  theme(legend.position="none")

In such cases it can be helpfull to add some noise to the points (position = "jitter") to reduce overlapping. This can be a powerfull approach, especially when combined with setting alpha()

gapminder %>% 
  dplyr::filter(continent == "Americas" |
                continent == "Africa") %>%
  dplyr::filter(year %in% years) %>%
    group_by(continent) %>%
  ggplot(aes(x = year,
             y = infant_mortality)) +
  geom_point(aes(colour = continent), position = "jitter") 

Bar chart

It would be nice to know what the mean child mortality is for both continents

gapminder %>% 
  dplyr::filter(continent == "Americas" |
                continent == "Africa") %>%
  dplyr::filter(year %in% years) %>%
  group_by(continent, year) %>%
  summarise(mean_infant_mortality = mean(infant_mortality, na.rm = TRUE)) %>% 
  ggplot(aes(x = year,
             y = mean_infant_mortality)) +
  geom_col(aes(fill = continent), position = "dodge") 

Adding summary data to an existing plot

Now that we have the mean infant mortality for each year for the two continents, let’s add that data to the previous dot plot where we used jitter

mean_inf_mort <- gapminder %>% 
  dplyr::filter(continent == "Americas" |
                continent == "Africa") %>%
  dplyr::filter(year %in% years) %>%
  group_by(continent, year) %>%
  summarise(mean_infant_mortality = mean(infant_mortality, na.rm = TRUE))

gapminder %>% 
  dplyr::filter(continent == "Americas" |
                continent == "Africa") %>%
  dplyr::filter(year %in% years) %>%
    group_by(continent) %>%
  ggplot(aes(x = year,
             y = infant_mortality)) +
  geom_point(aes(colour = continent), position = "jitter") +

## summary data added to previous 
  geom_line(data = mean_inf_mort, aes(x = year, 
                                      y = mean_infant_mortality, 
                                      colour = continent),  size = 2)

Filter data from a graph

In the figure above we can observe a number of countries in ‘Americas’ continent that have a child mortality that are above the average (over the years) of ‘Africa’. Which countries are this?

library(ggiraph)

gapminder$country <- 
  str_replace_all(string = gapminder$country, 
                pattern = "'", 
                replacement = "_")


interactive_inf_mort <- gapminder %>% 
  dplyr::filter(continent == "Americas" |
                continent == "Africa") %>%
  dplyr::filter(year %in% years) %>%
    group_by(region, country) %>%
  ggplot(aes(x = year,
             y = infant_mortality)) +
  
  geom_point_interactive(aes(tooltip = country, colour = region), position = "jitter") +
  
#  geom_point(aes(colour = continent), position = "jitter") +

## summary data added to previous 
 geom_line(data = mean_inf_mort, aes(x = year, 
                                      y = mean_infant_mortality, 
                                      colour = continent, group = continent),  size = 2
            )

interactive_inf_mort

gapminder$country %>% as_factor() %>% levels()

##   [1] "Albania"                        "Algeria"                       
##   [3] "Angola"                         "Antigua and Barbuda"           
##   [5] "Argentina"                      "Armenia"                       
##   [7] "Aruba"                          "Australia"                     
##   [9] "Austria"                        "Azerbaijan"                    
##  [11] "Bahamas"                        "Bahrain"                       
##  [13] "Bangladesh"                     "Barbados"                      
##  [15] "Belarus"                        "Belgium"                       
##  [17] "Belize"                         "Benin"                         
##  [19] "Bhutan"                         "Bolivia"                       
##  [21] "Bosnia and Herzegovina"         "Botswana"                      
##  [23] "Brazil"                         "Brunei"                        
##  [25] "Bulgaria"                       "Burkina Faso"                  
##  [27] "Burundi"                        "Cambodia"                      
##  [29] "Cameroon"                       "Canada"                        
##  [31] "Cape Verde"                     "Central African Republic"      
##  [33] "Chad"                           "Chile"                         
##  [35] "China"                          "Colombia"                      
##  [37] "Comoros"                        "Congo, Dem. Rep."              
##  [39] "Congo, Rep."                    "Costa Rica"                    
##  [41] "Cote d_Ivoire"                  "Croatia"                       
##  [43] "Cuba"                           "Cyprus"                        
##  [45] "Czech Republic"                 "Denmark"                       
##  [47] "Djibouti"                       "Dominican Republic"            
##  [49] "Ecuador"                        "Egypt"                         
##  [51] "El Salvador"                    "Equatorial Guinea"             
##  [53] "Eritrea"                        "Estonia"                       
##  [55] "Ethiopia"                       "Fiji"                          
##  [57] "Finland"                        "France"                        
##  [59] "French Polynesia"               "Gabon"                         
##  [61] "Gambia"                         "Georgia"                       
##  [63] "Germany"                        "Ghana"                         
##  [65] "Greece"                         "Greenland"                     
##  [67] "Grenada"                        "Guatemala"                     
##  [69] "Guinea"                         "Guinea-Bissau"                 
##  [71] "Guyana"                         "Haiti"                         
##  [73] "Honduras"                       "Hong Kong, China"              
##  [75] "Hungary"                        "Iceland"                       
##  [77] "India"                          "Indonesia"                     
##  [79] "Iran"                           "Iraq"                          
##  [81] "Ireland"                        "Israel"                        
##  [83] "Italy"                          "Jamaica"                       
##  [85] "Japan"                          "Jordan"                        
##  [87] "Kazakhstan"                     "Kenya"                         
##  [89] "Kiribati"                       "South Korea"                   
##  [91] "Kuwait"                         "Kyrgyz Republic"               
##  [93] "Lao"                            "Latvia"                        
##  [95] "Lebanon"                        "Lesotho"                       
##  [97] "Liberia"                        "Libya"                         
##  [99] "Lithuania"                      "Luxembourg"                    
## [101] "Macao, China"                   "Macedonia, FYR"                
## [103] "Madagascar"                     "Malawi"                        
## [105] "Malaysia"                       "Maldives"                      
## [107] "Mali"                           "Malta"                         
## [109] "Mauritania"                     "Mauritius"                     
## [111] "Mexico"                         "Micronesia, Fed. Sts."         
## [113] "Moldova"                        "Mongolia"                      
## [115] "Montenegro"                     "Morocco"                       
## [117] "Mozambique"                     "Namibia"                       
## [119] "Nepal"                          "Netherlands"                   
## [121] "New Caledonia"                  "New Zealand"                   
## [123] "Nicaragua"                      "Niger"                         
## [125] "Nigeria"                        "Norway"                        
## [127] "Oman"                           "Pakistan"                      
## [129] "Panama"                         "Papua New Guinea"              
## [131] "Paraguay"                       "Peru"                          
## [133] "Philippines"                    "Poland"                        
## [135] "Portugal"                       "Puerto Rico"                   
## [137] "Qatar"                          "Romania"                       
## [139] "Russia"                         "Rwanda"                        
## [141] "St. Lucia"                      "St. Vincent and the Grenadines"
## [143] "Samoa"                          "Saudi Arabia"                  
## [145] "Senegal"                        "Serbia"                        
## [147] "Seychelles"                     "Sierra Leone"                  
## [149] "Singapore"                      "Slovak Republic"               
## [151] "Slovenia"                       "Solomon Islands"               
## [153] "South Africa"                   "Spain"                         
## [155] "Sri Lanka"                      "Sudan"                         
## [157] "Suriname"                       "Swaziland"                     
## [159] "Sweden"                         "Switzerland"                   
## [161] "Syria"                          "Tajikistan"                    
## [163] "Tanzania"                       "Thailand"                      
## [165] "Timor-Leste"                    "Togo"                          
## [167] "Tonga"                          "Trinidad and Tobago"           
## [169] "Tunisia"                        "Turkey"                        
## [171] "Turkmenistan"                   "Uganda"                        
## [173] "Ukraine"                        "United Arab Emirates"          
## [175] "United Kingdom"                 "United States"                 
## [177] "Uruguay"                        "Uzbekistan"                    
## [179] "Vanuatu"                        "Venezuela"                     
## [181] "West Bank and Gaza"             "Vietnam"                       
## [183] "Yemen"                          "Zambia"                        
## [185] "Zimbabwe"

ggiraph(ggobj = interactive_inf_mort)

A more complicated example (for showing the capabilities of ggplot2)

west <- c("Western Europe","Northern Europe","Southern Europe",
          "Northern America","Australia and New Zealand")

gapminder <- gapminder %>%
  mutate(group = case_when(
    region %in% west ~ "The West",
    region %in% c("Eastern Asia", "South-Eastern Asia") ~ "East Asia",
    region %in% c("Caribbean", "Central America", "South America") ~ "Latin America",
    continent == "Africa" & region != "Northern Africa" ~ "Sub-Saharan Africa",
    TRUE ~ "Others"))
gapminder <- gapminder %>%
  mutate(group = factor(group, levels = rev(c("Others", "Latin America", "East Asia","Sub-Saharan Africa", "The West"))))

filter(gapminder, year%in%c(1962, 2013) & !is.na(group) &
         !is.na(fertility) & !is.na(life_expectancy)) %>%
  mutate(population_in_millions = population/10^6) %>%
  ggplot( aes(fertility, y=life_expectancy, col = group, size = population_in_millions)) +
  geom_point(alpha = 0.8) +
  guides(size=FALSE) +
  theme(plot.title = element_blank(), legend.title = element_blank()) +
  coord_cartesian(ylim = c(30, 85)) +
  xlab("Fertility rate (births per woman)") +
  ylab("Life Expectancy") +
  geom_text(aes(x=7, y=82, label=year), cex=12, color="grey") +
  facet_grid(. ~ year) +
  theme(strip.background = element_blank(),
        strip.text.x = element_blank(),
        strip.text.y = element_blank(),
   legend.position = "top")

Data Distributions & Outliers

Detecting outliers

For this part we use a different and more simple dataset This dataset contains 1192 observations on self-reported:

• height (inch)
• earn ($)
• sex (gender)
• ed (currently unannotated)
• age (years)
• race

heights_data <- read_csv(file = here::here("data",
                                          "heights_outliers.csv"))

heights_data

## # A tibble: 1,192 x 6
##     earn height sex       ed   age race    
##    <dbl>  <dbl> <chr>  <dbl> <dbl> <chr>   
##  1 50000   74.4 male      16    45 white   
##  2 60000   65.5 female    16    58 white   
##  3 30000   63.6 female    16    29 white   
##  4 50000   63.1 female    16    91 other   
##  5 51000   63.4 female    17    39 white   
##  6  9000   64.4 female    15    26 white   
##  7 29000   61.7 female    12    49 white   
##  8 32000   72.7 male      17    46 white   
##  9  2000   72.0 male      15    21 hispanic
## 10 27000   72.2 male      12    26 white   
## # ... with 1,182 more rows

Data characteristics

We will focus on the variable height here

summary_heights_data <- heights_data %>%
  group_by(sex, age) %>%
  summarise(mean_height = mean(height, na.rm = TRUE),
            min_height = min(height),
            max_height = max(height)) %>%
  arrange(desc(mean_height))

summary_heights_data[c(1:4),]

## # A tibble: 4 x 5
## # Groups:   sex [2]
##   sex      age mean_height min_height max_height
##   <chr>  <dbl>       <dbl>      <dbl>      <dbl>
## 1 female    55       141.        61.9      664. 
## 2 male      39       134.        66.6      572. 
## 3 male      55        73.2       71.7       74.8
## 4 male      91        73.1       73.1       73.1

From the above summary we can conclude that there are two outliers (presumably entry errors).

Calculate the height in meters for each outlier in the Console 1 inch = 0,0254 meters

Please discuss the solution with your neighbour

Checking the frequency distribution

heights_data %>%
  ggplot(aes(x = height)) +
  geom_histogram(aes(stat = "identity"), bins = 200)

This distribution looks odd. When you see a large x-axis with no data plotted on it, it usually means there is an outlier. If you look carefully, you will spot two outliers around 600

Boxplots to detect outliers

heights_data %>%
  ggplot(aes(y = height)) +
  geom_boxplot()

So apparantly there is one data point that is way off from the rest of the distribution. Let’s remove this point, using filter() from the {dplyr} package like we did before on the gapminder dataset.

heights_data %>%
  dplyr::filter(height < 100) %>%
  ggplot(aes(y = height)) +
  geom_boxplot()

## by sex

heights_data %>%
  dplyr::filter(height < 100) %>%
  ggplot(aes(y = height, x = sex)) +
  geom_boxplot()

New frequency distribution

Now let’s plot a new distribution plot, this time we plot density, leaving the outlier out

heights_data %>%
  dplyr::filter(height < 100) %>%
  ggplot(aes(height)) +
  geom_freqpoly(aes(y = ..density..))

## by sex
heights_data %>%
  dplyr::filter(height < 100) %>%
  ggplot(aes(height)) +
  geom_freqpoly(aes(y = ..density.., colour = sex))

A formal check to detect an outlier - handle with care

The best way to spot and take care of an outlier (you never delete an outlier unsubstantiated!!) is to create multiple visualizations as decribed above. However, there is a formal, simple statistical apporach to assess whether an observation is an outlier. The apporach below can only be used to detect a single outlier in a numeric vector. For more complex (multivariate) situations see e.g.: http://r-statistics.co/Outlier-Treatment-With-R.html

The Dixon’s Q Test for detecting outliers

The {outliers} package contains a number of tools. Here we will demonstrate the outlier() and dixon.test() functions. For more information on the DQT, see: https://www.statisticshowto.datasciencecentral.com/dixons-q-test/

outlier() function

This function finds the value with largest difference between it and sample mean, which can be an outlier.

We will walk trough a simple exercise that first shows the result of the outlier() function and than we calculate it by hand

First we create dummy data for a triplicate series of measurements. We assume 5 measurement values, replicated three times, containing one outlier. We also assume is was a concentration depended series in which the index indicates the used concentration.

set.seed(123)
## create data
random_numbers <- rnorm(n = 15, mean = 2.5, sd = 0.9) 
## inject outlier
random_numbers[4] <- 9.54
## define triplicates
triplicates <- rep(1:3, times = 5)
index <- rep(1:5, times = 3)

## put in dataframe
measured_data <- tibble(measured = random_numbers, replicate = triplicates,
                        index = index)
measured_data

## # A tibble: 15 x 3
##    measured replicate index
##       <dbl>     <int> <int>
##  1     2.00         1     1
##  2     2.29         2     2
##  3     3.90         3     3
##  4     9.54         1     4
##  5     2.62         2     5
##  6     4.04         3     1
##  7     2.91         1     2
##  8     1.36         2     3
##  9     1.88         3     4
## 10     2.10         1     5
## 11     3.60         2     1
## 12     2.82         3     2
## 13     2.86         1     3
## 14     2.60         2     4
## 15     2.00         3     5

## sort data
measured_data %>% 
  arrange(desc(measured))

## # A tibble: 15 x 3
##    measured replicate index
##       <dbl>     <int> <int>
##  1     9.54         1     4
##  2     4.04         3     1
##  3     3.90         3     3
##  4     3.60         2     1
##  5     2.91         1     2
##  6     2.86         1     3
##  7     2.82         3     2
##  8     2.62         2     5
##  9     2.60         2     4
## 10     2.29         2     2
## 11     2.10         1     5
## 12     2.00         3     5
## 13     2.00         1     1
## 14     1.88         3     4
## 15     1.36         2     3

Plotting the data reveals the outlier.

## plot
measured_data %>% 
#  enframe() %>%
  ggplot(aes(x = index, y = measured)) +
  geom_point() +
  geom_point(data = dplyr::filter(.data = measured_data, measured > 5), 
             colour = "red", size = 6, shape = 4)

Identify with outliers::outlier()

outliers::outlier(measured_data$measured)

## [1] 9.54

Identify with outliers::dixon.test()

## note that these functions from the {outliers} package take a vector (use $)
outliers::dixon.test(measured_data$measured)

## 
##  Dixon test for outliers
## 
## data:  measured_data$measured
## Q = 0.7472, p-value < 2.2e-16
## alternative hypothesis: highest value 9.54 is an outlier

The test is significant against the NULL hypothesis that the highest value is not an outlier. We can conclude that this NULL hypothesis does not hold on the basis of the significant p-value < 0.05

Manual calculations:

outlier()

mean_vctr <- mean(measured_data$measured)

distance <- measured_data$measured - mean_vctr

ind <- distance == max(distance)

measured_data[ind,]

## # A tibble: 1 x 3
##   measured replicate index
##      <dbl>     <int> <int>
## 1     9.54         1     4

dixon.test()

The Dixon’s Q Test can be calculated manually following the following procedure:

How to Run Dixon’s Q Test (R10).

Note: make sure your data set is normally distributed before running the test; for example, run a Shapiro-Wilk test. Running it on different distributions will lead to erroneous results. An extreme value may throw off any test for normality, so try running that test without the suspect data item. If your data set still doesn’t meet the assumption of normality after running a test for it, then you should not run Dixon’s Q Test.

Note that for very small sample sizes the assumtion for Normal distribution usually does not hold. We will see more formal way of establishing Normal distribution below.

Caution: the test should not be used more than once for the same set of data.

To perform the calculations:

Step 1: Sort your data into ascending order (smallest to largest).

Step 2 :Find the Q statistic using the following formula:

\(Qexp = \frac{x2 - x1}{xn - x1}\)

Where

• \(x1\) is the smallest (suspect) value
• \(x2\) is the second smallest value
• \(xn\) is the largest value

Dixon’s Q Test: Definition, Step by Step Examples + Q Critical Values Tables

Find Outliers > Dixon’s Q Test What is Dixon’s Q Test?

Dixon’s Q test, or just the “Q Test” is a way to find outliers in very small, normally distributed, data sets. Small data sets are usually defined as somewhere between 3 and 7 items. It’s commonly used in chemistry, where data sets sometimes include one suspect observation that’s much lower or much higher than the other values. Keeping an outlier in data affects calculations like the mean and standard deviation, so true outliers should be removed.

Dixon came up with many different equations to find true outliers. The most commonly used one is called the R10 or simply the “Q” version, which is used to test if one single value is an outlier in a sample size of between 3 and 7. Dean and Dixon did suggest various other formulas in a later paper, but these are not commonly used. For a full list of alternate formulas for different sample sizes (up to about 30), go to: Dixon’s Test, Alternate Formulas and Tables. How to Run Dixon’s Q Test (R10).

Note: make sure your data set is normally distributed before running the test; for example, run a Shapiro-Wilk test. Running it on different distributions will lead to erroneous results. An extreme value may throw off any test for normality, so try running that test without the suspect data item. If your data set still doesn’t meet the assumption of normality after running a test for it, then you should not run Dixon’s Q Test,

Caution: the test should not be used more than once for the same set of data.

Step 1: Sort your data into ascending order (smallest to largest). 167, 177, 180, 181, 185, 188, 189.

Step 2 :Find the Q statistic using the following formula: dixon’s q test statistic

Where:

x1 is the smallest (suspect) value,
x2 is the second smallest value,
and xn is the largest value.

Step 3: Find the Q critical value in the Q table (see below). For a sample size of 3 and an alpha level of 5%, the critical value is 0.970.

Step 4: Compare the Q statistic from Step 2 with the Q critical value in Step 3. If the Q statistic is greater than the Q critical value, the point is an outlier.

Let’s try it on our measured_data

measured_data_ranked <- measured_data %>%
  arrange(measured) 
measured_data_ranked$measured

##  [1] 1.361445 1.881832 1.995572 1.999743 2.098904 2.292840 2.599614
##  [8] 2.616359 2.823832 2.860694 2.914825 3.601674 3.902837 4.043558
## [15] 9.540000

x1 <- measured_data_ranked$measured[1]
x2 <- measured_data_ranked$measured[2]
xn <- max(measured_data_ranked$measured)

Qexp = (x2-x1) / (xn-x1)

##
0.5/8

## [1] 0.0625

Checking normality with a qqplot

a qqplot provides a visual aid to assess whether a distribution is approaching normality

## 
source(file = here::here("code", "ggqq.R"))
height_data_outlier_removed <- heights_data %>%
  dplyr::filter(height < 100)
  gg_qq(height_data_outlier_removed$height) 

##       25%       75% 
## 66.926998  4.328462

## formal statistical proof
shapiro.test(height_data_outlier_removed$height)

## 
##  Shapiro-Wilk normality test
## 
## data:  height_data_outlier_removed$height
## W = 0.98485, p-value = 8.491e-10

all data -> reject hypothesis that the sample has a normal distribution

Test individual distributions

males <- height_data_outlier_removed %>%
  dplyr::filter(sex == "male")

females <- height_data_outlier_removed %>%
  dplyr::filter(sex == "female")

shapiro.test(males$height)

## 
##  Shapiro-Wilk normality test
## 
## data:  males$height
## W = 0.99053, p-value = 0.002532

shapiro.test(females$height)

## 
##  Shapiro-Wilk normality test
## 
## data:  females$height
## W = 0.99277, p-value = 0.002105

## add shapiro for each sex

## we can do the same for age
shapiro.test(males$age)

## 
##  Shapiro-Wilk normality test
## 
## data:  males$age
## W = 0.93358, p-value = 3.506e-14

shapiro.test(females$age)

## 
##  Shapiro-Wilk normality test
## 
## data:  females$age
## W = 0.93978, p-value = 4.862e-16